Let's import our librairies¶

In [1]:
import pandas as pd
import numpy as np
import seaborn as sns
import matplotlib.pyplot as plt
import plotly.offline as pyo
pyo.init_notebook_mode()
import plotly.express as px
%matplotlib inline 

Load the data and explore it¶

In [2]:
df= pd.read_csv('moviestreams.csv')
In [3]:
df.head()
Out[3]:
Unnamed: 0 ID Title Year Age IMDb Rotten Tomatoes Netflix Hulu Prime Video Disney+ Type Directors Genres Country Language Runtime
0 0 1 Inception 2010 13+ 8.8 87% 1 0 0 0 0 Christopher Nolan Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French 148.0
1 1 2 The Matrix 1999 18+ 8.7 87% 1 0 0 0 0 Lana Wachowski,Lilly Wachowski Action,Sci-Fi United States English 136.0
2 2 3 Avengers: Infinity War 2018 13+ 8.5 84% 1 0 0 0 0 Anthony Russo,Joe Russo Action,Adventure,Sci-Fi United States English 149.0
3 3 4 Back to the Future 1985 7+ 8.5 96% 1 0 0 0 0 Robert Zemeckis Adventure,Comedy,Sci-Fi United States English 116.0
4 4 5 The Good, the Bad and the Ugly 1966 18+ 8.8 97% 1 0 1 0 0 Sergio Leone Western Italy,Spain,West Germany Italian 161.0
In [4]:
df.shape
Out[4]:
(16744, 17)
In [5]:
cols=df.columns.tolist()
cols
Out[5]:
['Unnamed: 0',
 'ID',
 'Title',
 'Year',
 'Age',
 'IMDb',
 'Rotten Tomatoes',
 'Netflix',
 'Hulu',
 'Prime Video',
 'Disney+',
 'Type',
 'Directors',
 'Genres',
 'Country',
 'Language',
 'Runtime']
In [6]:
df.info()
<class 'pandas.core.frame.DataFrame'>
RangeIndex: 16744 entries, 0 to 16743
Data columns (total 17 columns):
 #   Column           Non-Null Count  Dtype  
---  ------           --------------  -----  
 0   Unnamed: 0       16744 non-null  int64  
 1   ID               16744 non-null  int64  
 2   Title            16744 non-null  object 
 3   Year             16744 non-null  int64  
 4   Age              7354 non-null   object 
 5   IMDb             16173 non-null  float64
 6   Rotten Tomatoes  5158 non-null   object 
 7   Netflix          16744 non-null  int64  
 8   Hulu             16744 non-null  int64  
 9   Prime Video      16744 non-null  int64  
 10  Disney+          16744 non-null  int64  
 11  Type             16744 non-null  int64  
 12  Directors        16018 non-null  object 
 13  Genres           16469 non-null  object 
 14  Country          16309 non-null  object 
 15  Language         16145 non-null  object 
 16  Runtime          16152 non-null  float64
dtypes: float64(2), int64(8), object(7)
memory usage: 2.2+ MB
In [7]:
df.drop(['Unnamed: 0','ID'], axis=1, inplace=True)
In [8]:
df.columns
Out[8]:
Index(['Title', 'Year', 'Age', 'IMDb', 'Rotten Tomatoes', 'Netflix', 'Hulu',
       'Prime Video', 'Disney+', 'Type', 'Directors', 'Genres', 'Country',
       'Language', 'Runtime'],
      dtype='object')
In [9]:
df.isna().sum()
Out[9]:
Title                  0
Year                   0
Age                 9390
IMDb                 571
Rotten Tomatoes    11586
Netflix                0
Hulu                   0
Prime Video            0
Disney+                0
Type                   0
Directors            726
Genres               275
Country              435
Language             599
Runtime              592
dtype: int64
In [10]:
df.dtypes
Out[10]:
Title               object
Year                 int64
Age                 object
IMDb               float64
Rotten Tomatoes     object
Netflix              int64
Hulu                 int64
Prime Video          int64
Disney+              int64
Type                 int64
Directors           object
Genres              object
Country             object
Language            object
Runtime            float64
dtype: object

Changing Age column type from object to float, we need to remove the plus sign'+' before¶

In [11]:
df['Age'].value_counts()
Out[11]:
18+    3474
7+     1462
13+    1255
all     843
16+     320
Name: Age, dtype: int64
In [12]:
age_map={'18+':18, '7+':7,'13+':13,'all':0,'16+':16}
In [13]:
df['AgeCopy']=df['Age'].map(age_map)
In [14]:
df.AgeCopy
Out[14]:
0        13.0
1        18.0
2        13.0
3         7.0
4        18.0
         ... 
16739     NaN
16740     7.0
16741     NaN
16742     NaN
16743     NaN
Name: AgeCopy, Length: 16744, dtype: float64
In [15]:
df.drop('Age', axis=1, inplace=True)
In [16]:
df.rename(columns={'AgeCopy': 'Age'}, inplace=True)
In [17]:
df.head()
Out[17]:
Title Year IMDb Rotten Tomatoes Netflix Hulu Prime Video Disney+ Type Directors Genres Country Language Runtime Age
0 Inception 2010 8.8 87% 1 0 0 0 0 Christopher Nolan Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French 148.0 13.0
1 The Matrix 1999 8.7 87% 1 0 0 0 0 Lana Wachowski,Lilly Wachowski Action,Sci-Fi United States English 136.0 18.0
2 Avengers: Infinity War 2018 8.5 84% 1 0 0 0 0 Anthony Russo,Joe Russo Action,Adventure,Sci-Fi United States English 149.0 13.0
3 Back to the Future 1985 8.5 96% 1 0 0 0 0 Robert Zemeckis Adventure,Comedy,Sci-Fi United States English 116.0 7.0
4 The Good, the Bad and the Ugly 1966 8.8 97% 1 0 1 0 0 Sergio Leone Western Italy,Spain,West Germany Italian 161.0 18.0

Changing the Rotten Tomatoes type from object to float, and here, we need to remove the % sign¶

In [18]:
df['rotten_tomatoes']= df['Rotten Tomatoes'].str.replace('%','')
In [19]:
df['rotten_tomatoes']
Out[19]:
0         87
1         87
2         84
3         96
4         97
        ... 
16739    NaN
16740    NaN
16741    NaN
16742    NaN
16743    NaN
Name: rotten_tomatoes, Length: 16744, dtype: object
In [20]:
df.drop('Rotten Tomatoes', axis=1, inplace=True)
In [21]:
df['rotten_tomatoes']=df['rotten_tomatoes'].astype(float)
In [22]:
df.head()
Out[22]:
Title Year IMDb Netflix Hulu Prime Video Disney+ Type Directors Genres Country Language Runtime Age rotten_tomatoes
0 Inception 2010 8.8 1 0 0 0 0 Christopher Nolan Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French 148.0 13.0 87.0
1 The Matrix 1999 8.7 1 0 0 0 0 Lana Wachowski,Lilly Wachowski Action,Sci-Fi United States English 136.0 18.0 87.0
2 Avengers: Infinity War 2018 8.5 1 0 0 0 0 Anthony Russo,Joe Russo Action,Adventure,Sci-Fi United States English 149.0 13.0 84.0
3 Back to the Future 1985 8.5 1 0 0 0 0 Robert Zemeckis Adventure,Comedy,Sci-Fi United States English 116.0 7.0 96.0
4 The Good, the Bad and the Ugly 1966 8.8 1 0 1 0 0 Sergio Leone Western Italy,Spain,West Germany Italian 161.0 18.0 97.0

Visualisations¶

What is the number of movies for each Age group?¶

In [23]:
sns.countplot(df['Age'])
C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning:

Pass the following variable as a keyword arg: x. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

Out[23]:
<AxesSubplot:xlabel='Age', ylabel='count'>
In [24]:
df['Age'].value_counts()
Out[24]:
18.0    3474
7.0     1462
13.0    1255
0.0      843
16.0     320
Name: Age, dtype: int64

Top 10 languages¶

In [25]:
df['Language'].value_counts(ascending=False)[0:10]
Out[25]:
English            10955
Hindi                503
English,Spanish      276
Spanish              267
English,French       174
Italian              166
French               163
Japanese             155
Mandarin             151
Tamil                 93
Name: Language, dtype: int64
In [26]:
x= df['Language'].value_counts(ascending=False).index[0:10]
y= [df['Language'].value_counts(ascending=False)[i] for i in x]
plt.figure(figsize=(15,6))
sns.barplot(x,y)
plt.yticks(rotation=0)
plt.xticks(rotation=90)
plt.show()
C:\ProgramData\Anaconda3\lib\site-packages\seaborn\_decorators.py:36: FutureWarning:

Pass the following variables as keyword args: x, y. From version 0.12, the only valid positional argument will be `data`, and passing other arguments without an explicit keyword will result in an error or misinterpretation.

In [27]:
plt.figure(figsize=(10,8))
Out[27]:
<Figure size 1000x800 with 0 Axes>
<Figure size 1000x800 with 0 Axes>
In [28]:
language=df.Language.value_counts().head(10)
In [29]:
language
Out[29]:
English            10955
Hindi                503
English,Spanish      276
Spanish              267
English,French       174
Italian              166
French               163
Japanese             155
Mandarin             151
Tamil                 93
Name: Language, dtype: int64
In [30]:
plt.figure(figsize=(10,8))
sns.barplot(x=language.index, y=language.values)
plt.yticks(rotation=0)
plt.xticks(rotation=90)
plt.show()
In [31]:
from IPython.display import HTML
In [32]:
fig=px.pie(df, values=language.values, names=language.index, title='Top 10 languages in streaming movies')
In [33]:
fig
In [34]:
HTML(fig.to_html())
Out[34]:

Number of movies in specific age group in all service¶

In [35]:
fig1=px.bar(df, 
            x=df['Age'].value_counts().index,
            y=df['Age'].value_counts(), 
            title='Number of movies in specific age',
           text=df['Age'].value_counts(),
           height=600)
fig1.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig1

Number of movies in specific age group in Netflix¶

In [36]:
netflix= df[df['Netflix']==1]
In [37]:
netflix
Out[37]:
Title Year IMDb Netflix Hulu Prime Video Disney+ Type Directors Genres Country Language Runtime Age rotten_tomatoes
0 Inception 2010 8.8 1 0 0 0 0 Christopher Nolan Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French 148.0 13.0 87.0
1 The Matrix 1999 8.7 1 0 0 0 0 Lana Wachowski,Lilly Wachowski Action,Sci-Fi United States English 136.0 18.0 87.0
2 Avengers: Infinity War 2018 8.5 1 0 0 0 0 Anthony Russo,Joe Russo Action,Adventure,Sci-Fi United States English 149.0 13.0 84.0
3 Back to the Future 1985 8.5 1 0 0 0 0 Robert Zemeckis Adventure,Comedy,Sci-Fi United States English 116.0 7.0 96.0
4 The Good, the Bad and the Ugly 1966 8.8 1 0 1 0 0 Sergio Leone Western Italy,Spain,West Germany Italian 161.0 18.0 97.0
... ... ... ... ... ... ... ... ... ... ... ... ... ... ... ...
3555 Krish Trish and Baltiboy I 2009 NaN 1 0 0 0 0 Tilak Shetty Animation India Hindi,English 65.0 NaN NaN
3556 ChuChuTV Bedtime Stories & Moral Stories for K... 2019 NaN 1 0 0 0 0 NaN NaN NaN NaN NaN NaN NaN
3557 Elles étaient en guerre 1939-1945 2015 NaN 1 0 0 0 0 NaN NaN NaN NaN NaN NaN NaN
3558 Shikari 1991 NaN 1 0 0 0 0 Joydip Mukherjee,Zakir Hossain Action,Crime,Drama,Romance Bangladesh,India Bengali 145.0 NaN NaN
3559 Pocoyo Halloween: Space Halloween 2015 NaN 1 0 0 0 0 Alfonso Rodriguez Animation,Short United States English 39.0 NaN NaN

3560 rows × 15 columns

In [38]:
fig2=px.bar(df, 
            x=netflix['Age'].value_counts().index,
            y=netflix['Age'].value_counts(), 
            title='Number of movies in specific age in Netflix',
           text=netflix['Age'].value_counts(),
           height=600)
fig2.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig2

Number of movies in specific age group in Amazon Prime¶

In [39]:
prime_video= df[df['Prime Video']==1]
In [40]:
fig3=px.bar(df, 
            x=prime_video['Age'].value_counts().index,
            y=prime_video['Age'].value_counts(), 
            title='Number of movies in specific age in Amazon Prime Video',
           text=prime_video['Age'].value_counts(),
           height=600)
fig3.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig3

Number of movies in specific age group in Hulu¶

In [41]:
hulu= df[df['Hulu']==1]
In [42]:
fig4= px.bar(x=hulu['Age'].value_counts().index,
             y=hulu['Age'].value_counts(),
             title='Number of movies in specific age in Hulu',
             text=hulu['Age'].value_counts(),
           height=600)
fig4.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig4
In [43]:
disney_plus= df[df['Disney+']==1] 
In [44]:
fig5= px.bar(x=disney_plus['Age'].value_counts().index,
             y=disney_plus['Age'].value_counts(),
             title='Number of movies in specific age in Disney+',
             text=disney_plus['Age'].value_counts(),
           height=600)
fig5.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig5

Rotten Tomatoes Ratings for overall service¶

In [45]:
fig6= px.bar(x=df['rotten_tomatoes'].value_counts().index,
            y=df['rotten_tomatoes'].value_counts(),
            title='Rotten Tomatoes Ratings for overall service',
            text=df['rotten_tomatoes'].value_counts(),
            height=600)
fig6.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig6

Now let's do rotten tomatoes ratings for each service¶

Netflix¶

In [46]:
fig7= px.bar(x=netflix['rotten_tomatoes'].value_counts().index,
            y=netflix['rotten_tomatoes'].value_counts(),
            title='Rotten Tomatoes ratings for Movies on Netflix',
            text=netflix['rotten_tomatoes'].value_counts(),
            height=600
            )
fig7.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig7

Amazon Prime Video¶

In [47]:
fig8= px.bar(x=prime_video['rotten_tomatoes'].value_counts().index,
            y=prime_video['rotten_tomatoes'].value_counts(),
            title='Rotten Tomatoes ratings for Movies on Amazon Prime Video',
            text=prime_video['rotten_tomatoes'].value_counts(),
            height=600
            )
fig8.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig8

Disney+¶

In [48]:
fig9= px.bar(x=disney_plus['rotten_tomatoes'].value_counts().index,
            y=disney_plus['rotten_tomatoes'].value_counts(),
            title='Rotten Tomatoes ratings for Movies on Disney+',
            text=disney_plus['rotten_tomatoes'].value_counts(),
            height=600
            )
fig9.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig9

Hulu¶

In [49]:
fig10= px.bar(x=hulu['rotten_tomatoes'].value_counts().index,
            y=hulu['rotten_tomatoes'].value_counts(),
            title='Rotten Tomatoes ratings for Movies on Hulu',
            text=hulu['rotten_tomatoes'].value_counts(),
            height=600
            )
fig10.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig10

We can also proceed this way to display Rotten Tomatoes ratings for each service¶

In [50]:
rt_scores=pd.DataFrame({'Streaming Service':['Prime Video','Hulu','Disney+','Netflix'],
                       'Rotten Tomatoes Score':[prime_video['rotten_tomatoes'].value_counts().iloc[0],
                                                hulu['rotten_tomatoes'].value_counts().iloc[0],
                                                disney_plus['rotten_tomatoes'].value_counts().iloc[0],
                                                netflix['rotten_tomatoes'].value_counts().iloc[0]]})
In [51]:
rt_scores.head()
Out[51]:
Streaming Service Rotten Tomatoes Score
0 Prime Video 257
1 Hulu 18
2 Disney+ 19
3 Netflix 130
In [52]:
sort_rtscores= rt_scores.sort_values(ascending=False, by='Rotten Tomatoes Score')
In [53]:
sort_rtscores
Out[53]:
Streaming Service Rotten Tomatoes Score
0 Prime Video 257
3 Netflix 130
2 Disney+ 19
1 Hulu 18
In [54]:
fig11= px.bar(x=sort_rtscores['Streaming Service'],
            y=sort_rtscores['Rotten Tomatoes Score'],
            title='Number of Movies with 100 as Rotten tomatoes rating for each Streaming Services',
            text=sort_rtscores['Rotten Tomatoes Score'],
            height=600
            )
fig11.update_traces(marker_color='red',texttemplate='%{text:.2s}', textposition='outside')
fig11

IMDB Ratings¶

In [55]:
fig12= px.bar(x=df['IMDb'].value_counts().index,
              y=df['IMDb'].value_counts(),
              title='Overall IMDb Ratings',
              text=df['IMDb'].value_counts(),
              height=600)
fig12.update_traces(texttemplate='%{text:.2s}', textposition='outside', marker_color='green')
fig12

IMDb Rating for movies on Netflix¶

In [56]:
fig13= px.bar(x=netflix['IMDb'].value_counts().index,
            y=netflix['IMDb'].value_counts(),
            title='IMDb ratings for Movies on Netflix',
            text=netflix['IMDb'].value_counts(),
            height=600
            )
fig13.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig13

IMDb Rating for movies in Hulu¶

In [57]:
fig14= px.bar(x=hulu['IMDb'].value_counts().index,
            y=hulu['IMDb'].value_counts(),
            title='IMDb ratings for Movies on Hulu',
            text=hulu['IMDb'].value_counts(),
            height=600
            )
fig14.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig14

IMDb Rating for movies on Disney+¶

In [58]:
fig15= px.bar(x=disney_plus['IMDb'].value_counts().index,
            y=disney_plus['IMDb'].value_counts(),
            title='IMDb ratings for Movies on Disney+',
            text=disney_plus['IMDb'].value_counts(),
            height=600
            )
fig15.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig15

IMDb Rating for movies on Amazopn Prime Video¶

In [59]:
fig16= px.bar(x=prime_video['IMDb'].value_counts().index,
            y=prime_video['IMDb'].value_counts(),
            title='IMDb ratings for Movies on Amazon Prime Video',
            text=prime_video['IMDb'].value_counts(),
            height=600
            )
fig16.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig16

Count of Runtimes of Movies¶

In [60]:
CountRuntimes=pd.DataFrame(dict(df['Runtime'].value_counts().sort_values(ascending=False)[:10]).items(), 
                          columns=['Runtime', 'Count'])
In [61]:
CountRuntimes
Out[61]:
Runtime Count
0 90.0 971
1 95.0 489
2 92.0 434
3 93.0 422
4 85.0 408
... ... ...
152 19.0 8
153 32.0 8
154 9.0 8
155 7.0 8
156 10.0 8

157 rows × 2 columns

In [62]:
fig17= px.bar(x=CountRuntimes['Runtime'],
            y=CountRuntimes['Count'],
            title='Count of Runtimes of movies',
            text=CountRuntimes['Count'],
            height=600
            )
fig17.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig17

Directors and their count of movies they have directed¶

In [63]:
df['Directors']=df['Directors'].astype(str)
In [64]:
directors_df= df[df['Directors'] != np.nan ]
In [65]:
directors_df.head()
Out[65]:
Title Year IMDb Netflix Hulu Prime Video Disney+ Type Directors Genres Country Language Runtime Age rotten_tomatoes
0 Inception 2010 8.8 1 0 0 0 0 Christopher Nolan Action,Adventure,Sci-Fi,Thriller United States,United Kingdom English,Japanese,French 148.0 13.0 87.0
1 The Matrix 1999 8.7 1 0 0 0 0 Lana Wachowski,Lilly Wachowski Action,Sci-Fi United States English 136.0 18.0 87.0
2 Avengers: Infinity War 2018 8.5 1 0 0 0 0 Anthony Russo,Joe Russo Action,Adventure,Sci-Fi United States English 149.0 13.0 84.0
3 Back to the Future 1985 8.5 1 0 0 0 0 Robert Zemeckis Adventure,Comedy,Sci-Fi United States English 116.0 7.0 96.0
4 The Good, the Bad and the Ugly 1966 8.8 1 0 1 0 0 Sergio Leone Western Italy,Spain,West Germany Italian 161.0 18.0 97.0
In [66]:
directors_moviescount=dict()
In [67]:
director_list=list(directors_df['Directors'])
In [68]:
director_list
Out[68]:
['Christopher Nolan',
 'Lana Wachowski,Lilly Wachowski',
 'Anthony Russo,Joe Russo',
 'Robert Zemeckis',
 'Sergio Leone',
 'Bob Persichetti,Peter Ramsey,Rodney Rothman',
 'Roman Polanski',
 'Quentin Tarantino',
 'Steven Spielberg',
 'Quentin Tarantino',
 'Martin Scorsese',
 'Rajkumar Hirani',
 'Guillermo del Toro',
 'Lenny Abrahamson',
 'Terry Gilliam,Terry Jones',
 'Sergio Leone',
 'Steven Spielberg',
 'Harold Ramis',
 'Tom Hooper',
 'Spike Jonze',
 'Paul Thomas Anderson',
 'David Fincher',
 'Jean-Marc Vallée',
 'Stephen Chbosky',
 'Terry Jones',
 'Alex Garland',
 'David O. Russell',
 'Neill Blomkamp',
 'Martin Scorsese',
 'Duncan Jones',
 'Noah Baumbach',
 'Nicolas Winding Refn',
 'Sang-ho Yeon',
 'Brad Bird',
 'Nitesh Tiwari',
 'Sergio Pablos,Carlos Martínez López',
 'Steven Spielberg',
 'Clint Eastwood',
 'Aamir Khan,Amole Gupte',
 'John Hughes',
 'Pierre Coffin,Chris Renaud',
 'David Mackenzie',
 'Frank Miller,Robert Rodriguez,Quentin Tarantino',
 'Wilson Yip',
 'Steven Spielberg',
 'James Marsh',
 'Gabriele Muccino',
 'Asif Kapadia',
 'Edgar Wright',
 'Paul Thomas Anderson',
 'Quentin Tarantino',
 'Michel Hazanavicius',
 'Barry Jenkins',
 'Robert Zemeckis',
 'Mel Stuart',
 'Lasse Hallström',
 'David Fincher',
 'Naoko Yamada',
 'nan',
 'Martin Scorsese',
 'Peyton Reed',
 'James L. Brooks',
 'Robert Zemeckis',
 'Malik Bendjelloul',
 'Sam Mendes',
 'Susan Johnson',
 'Aaron Sorkin',
 'Gabriela Cowperthwaite',
 'Vince Gilligan',
 'Oriol Paulo',
 'Tomm Moore',
 'David Ayer',
 'Sam Raimi',
 'Richard Curtis',
 'Stephen Chow',
 'Abdellatif Kechiche',
 'Alfonso Cuarón',
 'David Zucker',
 'Jeff Nichols',
 'Kelly Fremon Craig',
 'nan',
 'Ashutosh Gowariker',
 'Eli Craig',
 'Lana Wachowski,Lilly Wachowski',
 'Fernando Meirelles',
 'Edward Zwick',
 'Sean Baker',
 'Ava DuVernay',
 'S.S. Rajamouli',
 'Josh Lowell,Peter Mortimer',
 'Craig Brewer',
 'David Yates',
 'Galder Gaztelu-Urrutia',
 'Bong Joon Ho',
 'nan',
 'John Lee Hancock',
 'Bryan Fogel',
 'David Gelb',
 'Tim Burton,Mike Johnson',
 'Cameron Crowe',
 'Armando Iannucci',
 'Martin Campbell',
 'Mark Herman',
 'nan',
 'Phil Johnston,Rich Moore',
 'Benny Safdie,Josh Safdie',
 'Wilson Yip',
 'Mark Osborne',
 'James Ponsoldt',
 'S.S. Rajamouli',
 'David Slade',
 'Rajkumar Hirani',
 'Sam Hargrave',
 'Yorgos Lanthimos',
 'Derek Cianfrance',
 'Zoya Akhtar',
 'Jeremy Saulnier',
 'Jennifer Yuh Nelson',
 'Ethan Coen,Joel Coen',
 'Matt Reeves',
 'Chang-dong Lee',
 'Steven Spielberg',
 'Byron Howard,Chris Williams',
 'Hideaki Anno,Kazuya Tsurumaki',
 'Kevin Smith',
 'Ron Clements,John Musker',
 'Nattawut Poonpiriya',
 'Robin Hardy',
 'Tom Ford',
 'Ted Demme',
 'Noah Baumbach',
 'Claude Barras',
 'Chris Smith',
 'Anurag Basu',
 'Noah Baumbach',
 'Sydney Pollack',
 'Ron Howard',
 'Danny Boyle',
 'Ron Underwood',
 'Kirk DeMicco,Chris Sanders',
 'Steven Knight',
 'Jay Karas',
 'David Mackenzie',
 'Dee Rees',
 'Jay Roach',
 'Jeremy Saulnier',
 'Roland Emmerich',
 'Alejandro G. Iñárritu',
 'Ruben Östlund',
 'Mike Flanagan',
 "Gavin O'Connor",
 'Ashutosh Gowariker',
 'Tomas Alfredson',
 'Robert Eggers',
 'Vishal Bhardwaj',
 'R. Balki',
 'Mike Flanagan',
 'Shekhar Kapur',
 'Lana Wilson',
 'Marc Forster',
 'Jérémy Clapin',
 'Grant Sputore',
 'Hayao Miyazaki',
 'André Øvredal',
 'Orlando von Einsiedel',
 'Richard LaGravenese',
 'Paul Thomas Anderson',
 'Ethan Coen,Joel Coen',
 'David Michôd',
 'Nora Twomey',
 'Chiwetel Ejiofor',
 'Anubhav Sinha',
 'Yorgos Lanthimos',
 'John Hillcoat',
 'Karan Johar',
 'Tim Burton',
 'Zach Lipovsky,Adam B. Stein',
 'Jon Olb,Madeleine Parry',
 'Martin Campbell',
 'Barry Levinson',
 'Kurt Wimmer',
 'Sarah Smith,Barry Cook',
 'Francis Ford Coppola',
 'Evgeny Afineevsky',
 'Nahnatchka Khan',
 'Neil Jordan',
 'Steven Spielberg',
 'Ric Roman Waugh',
 'Karyn Kusama',
 'J.C. Chandor',
 'Adam McKay',
 'Shakun Batra',
 'Ildikó Enyedi',
 'Rob Burnett',
 'James Wan',
 'Mike Binder',
 'Michael Bay',
 'Martin Campbell',
 'Tom Hooper',
 'Colm McCarthy',
 'Susanne Bier',
 'Gore Verbinski',
 'Scott Cooper',
 'Nagraj Manjule',
 'Jay Roach',
 'Daniel Lindsay,T.J. Martin',
 'Mike Mills',
 'Nikkhil Advani',
 'Dan Kwan,Daniel Scheinert',
 'Sunao Katabuchi',
 'David Lowery',
 'Paul Greengrass',
 'Phillip Noyce',
 'Chris Renaud,Jonathan del Val',
 'Andy Tennant',
 'nan',
 'Denis Villeneuve',
 'Oriol Paulo',
 'Sebastian Jones,Ramez Silyan',
 'Steven Spielberg',
 'Mike Newell',
 'Macon Blair',
 'Justin Benson,Aaron Moorhead',
 'Nora Ephron',
 'Louie Psihoyos',
 'Babak Anvari',
 'Ric Roman Waugh',
 'Joachim Rønning,Espen Sandberg',
 'Rob Marshall',
 'Paul Katis',
 'David Sington',
 'Timo Tjahjanto',
 'Jean-François Richet',
 'Amar Kaushik',
 'Steven Bognar,Julia Reichert',
 'Jeff Chan',
 'Phillip Noyce',
 'Lasse Hallström',
 'Gastón Duprat,Mariano Cohn',
 'Hiroshi Aoyama,Kazumi Fukushima,Jim Stenstrum',
 'Jeff Orlowski',
 'James Ponsoldt',
 'Charlie McDowell',
 'Len Wiseman',
 'Cristi Puiu',
 'Eric Darnell,Tom McGrath',
 'Antonin Baudry',
 'Tommy Wirkola',
 'Kris Pearn,Cory Evans,Rob Lodermeier',
 'Orlando von Einsiedel',
 'nan',
 'Lana Wachowski,Lilly Wachowski',
 'Jeff Margolis',
 'Jay Roach',
 'Nicholas Stoller',
 'John Maclean',
 'Noah Baumbach',
 'Chapman Way,Maclain Way',
 'Richard Shepard',
 'Wilson Yip',
 'Scott Derrickson',
 'Sam Dunn,Scot McFadyen',
 'Morgan Neville',
 'Kyle Patrick Alvarez',
 'Simon Curtis',
 'Henry Joost,Ariel Schulman',
 'Advait Chandan',
 'Tamara Jenkins',
 'Sam Mendes',
 'Peyton Reed',
 'Adam Nimoy',
 'Michael Dowse',
 'Tom Shadyac',
 'Jacob Kornbluth',
 'Antoine Fuqua',
 'Stan Lathan',
 'Alex Timbers',
 'Luis Valdez',
 'Michael Mann',
 'Mat Whitecross',
 'Petra Costa',
 'Robert Zemeckis',
 'Jonathan Glazer',
 'James Lebrecht,Nicole Newnham',
 'Ed Perkins',
 'Raymie Muzquiz,Stu Livingston',
 'Kevin Smith',
 'Frank Oz',
 'John Irvin',
 'Shane Acker',
 'Daniel Lindsay,T.J. Martin',
 'Jon Greenhalgh',
 'Will Becher,Richard Phelan',
 'Christian Desmares,Franck Ekinci',
 'John Lee Hancock',
 'Brian Helgeland',
 'Alê Abreu',
 'Patrick Brice',
 'Jonathan Mostow',
 'Jason Todd Ipson',
 'Mani Ratnam',
 'Louis C.K.',
 'Álvaro Brechner',
 'Alice Rohrwacher',
 'Liz Garbus',
 'Alex Lehmann',
 'Zak Hilditch',
 'Peter Berg',
 'Rachel Mason',
 'Ethan Coen,Joel Coen',
 'Leslie Iwerks',
 'Lars Klevberg',
 'Thom Zimny',
 'Thurop Van Orman,John Rice',
 'Gillian Robespierre',
 'Prasanth Varma',
 'Andrea Arnold',
 'Richard da Costa,Alex Parkinson',
 'Abhishek Chaubey',
 'Christian Gudegast',
 'Haifaa Al-Mansour',
 'Mark Waters',
 'Clay Kaytis',
 'David Soren',
 'Howard Zieff',
 'Paul Greengrass',
 'Steven Brill,Nicholaus Goossen',
 'Atom Egoyan',
 'J.C. Chandor',
 'Alexandra Dean',
 'Alex Lehmann',
 'Michael Bay',
 'Larry David',
 'Ted Braun',
 'Abhinay Deo,Akshat Verma',
 'Constance Marks,Philip Shane',
 'Morgan Matthews',
 'Trey Edward Shults',
 'Jon Alpert',
 'Patrick Brice',
 'Evan Goldberg,Seth Rogen',
 'Gabriel Clarke,Torquil Jones',
 'Wolfgang Petersen',
 'Craig Brewer',
 'David Ayer',
 'Ben Howling,Yolanda Ramke',
 'Icíar Bollaín',
 'Bernard Rose',
 'Hiromasa Yonebayashi,Giles New',
 'Matt Palmer',
 'Angelina Jolie',
 'Rene Liu',
 'Lou Adler,Tommy Chong',
 'James Wong',
 'Michael Cuesta',
 'Tiller Russell',
 'Bert Marcus',
 'David Mackenzie',
 'Robert Luketic',
 'Neil Armfield',
 'Trey Edward Shults',
 'Noah Baumbach,Jake Paltrow',
 'Chih-Yen Hsu,Mag Hsu',
 'Roger Spottiswoode',
 'Elizabeth Banks,Steven Brill,Steve Carr,Rusty Cundieff,James Duffy,Griffin Dunne,Peter Farrelly,Patrik Forsberg,Will Graham,James Gunn,Brett Ratner,Jonathan van Tulleken,Bob Odenkirk',
 'Ofir Raul Graizer',
 'Eugenio Derbez',
 'Ben Younger',
 'Lukas Dhont',
 'David Zucker',
 'Ashwiny Iyer Tiwari',
 'Shonali Bose',
 'Ken Hughes',
 'Neeraj Pandey',
 'Ashutosh Gowariker',
 'John Butler',
 'Christian Ditter',
 'Jacques Perrin,Jacques Cluzaud',
 'David Bruckner',
 'Kevin Phillips',
 'Sian Heder',
 'Chris Kelly',
 'Nick Cassavetes',
 'Nic Mathieu',
 'McG',
 'Joe Berlinger',
 'Jerrold Tarog',
 'Frank Coraci',
 'Saul Dibb',
 'Mark Craig',
 'Nate Adams,Adam Carolla',
 'Alexandre Heboyan,Benoît Philippon',
 'Stephanie Soechtig,Jeremy Seifert',
 'Ron Howard',
 'Estevan Oriol',
 'Juan Pablo Rebella,Pablo Stoll',
 'Wayne Blair',
 'Newt Arnold',
 'Steve Loter',
 'Dylan Bank,Daniel DiMauro,Morgan Pehme',
 'Craig Johnson',
 'Jim Field Smith',
 'Gilles Paquet-Brenner',
 'Nana Ekvtimishvili,Simon Groß',
 'Andrew Dominik',
 'Abhay Chopra',
 'Michael Bay',
 'Matthew Shoychet',
 'Andrew Slater',
 'Jeremy Saulnier',
 'Sarah Gavron',
 'Richie Smyth',
 'McG',
 'Genndy Tartakovsky',
 'Tony Zierra',
 'Ritesh Batra',
 'Eddie Mensore',
 'Asghar Farhadi',
 'nan',
 'Brett Harvey',
 'Peggy Holmes',
 'Michael Apted',
 'Saul Dibb',
 'Rayka Zehtabchi',
 'Barry W. Blaustein',
 'Kevin R. Adams,Joe Ksander,Ricardo Curtis',
 'Daniel Goldhaber',
 'Houda Benyamina',
 'Sandi Tan',
 'Len Wiseman',
 'Farah Khan',
 'Jerry Rothwell',
 'John Cromwell',
 'Milind Dhaimade',
 'Gauri Shinde',
 'Prentice Penny',
 'Russell Crowe',
 'Chris Moukarbel',
 'Jay Roach',
 'Jason Krawczyk',
 'Kevin Macdonald',
 'Jeffrey Walker',
 'Paul W.S. Anderson',
 'Anna Boden,Ryan Fleck',
 'Morgan Neville',
 'Abhishek Kapoor',
 'Sujoy Ghosh',
 'Jason Reitman',
 'Sam Dunn',
 'Wes Craven',
 'Jeff Tremaine',
 'Stephen Chiodo',
 'David Leveaux',
 'Peter Berg',
 'Jeremy Rush',
 'Noah Baumbach',
 'Lee Tamahori',
 'Kar-Wai Wong',
 'Dava Whisenant',
 'Darnell Martin',
 'Jeffrey Schwarz',
 'Felix Thompson',
 'Floria Sigismondi',
 'Roland Emmerich',
 'Alice Wu',
 'Alan Yang',
 'Leslye Headland',
 'Robert Zemeckis',
 'Brad Anderson',
 'Woo-Ping Yuen',
 'Pablo Larraín',
 'Lone Scherfig',
 'Roger Kumble',
 'nan',
 'Daniel Raim',
 'Ron Howard',
 'Tanuja Chandra',
 'Cary Joji Fukunaga',
 'Mark Dennis,Ben Foster',
 'David R. Ellis',
 'Marcus Raboy',
 'Jeffrey Walker',
 'Randal Kleiser',
 'Brett Haley',
 'Andrey Zvyagintsev',
 'Patrick Tatopoulos',
 'Reema Kagti',
 'Leslie Small,Tim Story',
 'Phil Joanou',
 'Akiva Schaffer',
 'Simon J. Smith,Steve Hickner',
 'Stephen Daldry,Christian Duurvoort',
 'Mar Targarona',
 'Ken Kwapis',
 'Lee Fulkerson',
 'Barry Levinson',
 'Rajkumar Hirani',
 'Hiroyuki Seshita',
 'nan',
 'Rob Cohen',
 'Chris Baugh',
 'Jeremiah Zagar',
 'Sean McNamara',
 'Joe Murray,Cosmo Segurson',
 'Steven Soderbergh',
 'Aniruddha Roy Chowdhury',
 'Rob Minkoff',
 'Zachary Heinzerling',
 'Romain Gavras',
 'Cate Shortland',
 'David Ayer',
 'Ken Kwapis',
 'Adam Leon',
 'Abhishek Sharma',
 'Thomas Vinterberg',
 'Mimi Leder',
 'Sara Colangelo',
 'Brad Anderson',
 'nan',
 'Frant Gwo',
 'Erik Canuel',
 'Bob Odenkirk',
 'John Curran',
 'Stanley Nelson',
 'David Fairhead',
 'Afia Nathaniel',
 'John Milius',
 'Sanjay Gupta',
 'John Wells',
 'Mark Christopher',
 'Jon Gunn',
 'Lynn Shelton',
 'Andy Serkis',
 'Paul Urkijo Alijo',
 'John A. Davis',
 'Oliver Stone',
 'Nila Madhab Panda',
 'Anusha Rizvi,Mahmood Farooqui',
 'John Dower',
 'nan',
 'Paul W.S. Anderson',
 'Daniel J. Clark',
 'Nicolas Cage',
 'David Pastor,Àlex Pastor',
 'Kristina Goolsby,Ashley York',
 'Bobcat Goldthwait',
 'Nicholas Zeig-Owens',
 'Karthik Subbaraj',
 'Tommy Avallone',
 'Harry Elfont,Deborah Kaplan',
 'Bob Nelson',
 'Hugh Wilson',
 'Florian Gallenberger',
 'Ben Wheatley',
 'Greg Berlanti',
 'Michael Fimognari',
 'Jon Watts',
 'James Bridges',
 'Jeff Pollack',
 'Billy Corben',
 'Barry Avrich',
 'Larry Peerce',
 'Russell Mulcahy',
 'Michael Cuesta',
 'Rakeysh Omprakash Mehra',
 'Roger Allers,Jill Culton,Anthony Stacchi',
 'Yance Ford',
 'Alexander Witt',
 'Alberto Rodríguez',
 'Kevin Macdonald',
 'James Mangold',
 'Jeremy Coon,Tim Skousen',
 'Peter Hutchings',
 'Sion Sono',
 'Kasper Collin',
 'Richard Benjamin',
 'Ariel Vromen',
 'Steve Antin',
 'Gastón Duprat',
 'Bruce Beresford',
 'Kyle Newacheck',
 'Bonni Cohen,Jon Shenk',
 'Keiichi Hara,Stephanie Sheh,Michael Sinterniklaas',
 'Anne Fletcher',
 'Ricky Gervais,Matthew Robinson',
 'Simon Baker',
 'Mark Raso',
 'Dante Lam',
 'Jeremy Saulnier',
 'Barak Goodman,Jamila Ephron',
 'Lynn Shelton',
 'Francesco Cinquemani,George Gallo',
 'Kip Andersen,Keegan Kuhn',
 'Bruce Gowers',
 'Esteban Sapir',
 'Zackary Canepari,Drea Cooper',
 'Robert Altman',
 'Mike Flanagan',
 'Mijke de Jong',
 'Jennifer Peedom',
 'Nicholas Hytner',
 'Paco Cabezas',
 'Imtiaz Ali',
 'Ole Bornedal',
 'Dustin Hoffman',
 'Farhan Akhtar',
 'Eva Orner',
 'Charles Martin Smith',
 'Raj Kumar Gupta',
 'Benjamin Turner,Gabe Turner',
 'Ivan Reitman',
 'Erik Matti',
 'Jennifer Phang',
 'Phyllida Lloyd',
 'Julius Avery',
 'Stig Bergqvist,Paul Demeyer',
 'Alessio Cremonini',
 'J.D. Dillard',
 'Rahul Dholakia',
 'David Michôd',
 'Tigmanshu Dhulia',
 'Orson Welles',
 'Makoto Shinkai',
 'Peter Spirer',
 'John Ridley',
 'Nandita Das',
 'Nagesh Kukunoor',
 'Alex Smith,Andrew J. Smith',
 'Will Allen',
 'Jenny Gage',
 'Clint Eastwood',
 'Haoling Li,Yoshitaka Takeuch,Xiaoxing Yi',
 'Haifaa Al-Mansour',
 'James Wong',
 'Beeban Kidron',
 'Shekhar Kapur',
 'Hikari',
 'David Wain',
 'Michel Gondry',
 'Lasse Hallström',
 'Elaine McMillion Sheldon',
 'Sanjay Leela Bhansali',
 'Malik Vitthal',
 'Scott Aukerman',
 'Daniel Stamm',
 'Gary Fleder',
 'Drake Doremus',
 'Noël Wells',
 'Jonathan Demme',
 'Jonathan Liebesman',
 'Greg Campbell',
 'Ari Sandel',
 'Lyric R. Cabral,David Felix Sutcliffe',
 'Karan Johar',
 'Mike Clattenburg',
 'Ömer Faruk Sorak',
 'Jean-Loup Felicioli,Alain Gagnol',
 'Matthew Cooke',
 'Wally Pfister',
 'Anurag Basu',
 'Chris Nelson',
 'Bille August',
 'Sujoy Ghosh',
 'Rajkumar Santoshi',
 'Luke Snellin',
 'Steve Carr',
 'Jeff Baena',
 'Milind Rau',
 'Errol Morris',
 'Roger Allers,Gaëtan Brizzi,Paul Brizzi,Joan C. Gratz,Mohammed Saeed Harib,Tomm Moore,Nina Paley,Bill Plympton,Joann Sfar,Michal Socha',
 'Mike Rohl',
 'Oz Perkins',
 'Bert Marcus,Cyrus Saidi',
 'Karen Moncrieff',
 'Patricia Rozema',
 'Jedd Wider,Todd Wider',
 'Rob Minkoff',
 'Paul Dugdale',
 'Elite Zexer',
 'Pierre Morel',
 'Zoya Akhtar',
 'Mary Mazzio',
 'John Lee',
 'Gus Van Sant',
 'Susan Johnson',
 'Roberta Grossman,Sophie Sartain',
 'Ivan Sen',
 'William Eubank',
 'Gideon Raff',
 'Tarsem Singh',
 'Blitz Bazawule',
 'Jeff Baena',
 'Walter Hill',
 'Griffin Dunne',
 'Aaron Hann,Mario Miscione',
 'Bo Burnham',
 'Bryan Bertino',
 'Greg Barker',
 'nan',
 'Ryan Polito,Jake Szymanski',
 'Jim Mickle',
 'Tarsem Singh',
 'Vinil Mathew',
 'Vikramaditya Motwane',
 'Antoine Fuqua',
 'Robin Swicord',
 'Zach Braff',
 'Masaaki Yuasa',
 'Keanu Reeves',
 'Christian Charles',
 'Cyril Frankel',
 'Pascal Laugier',
 'Louis C.K.',
 'Peter Middleton,James Spinney',
 'Vince Marcello',
 'Alexandre Aja',
 'Michael Thelin',
 'Lonny Price',
 'Doug Liman',
 'Olivia Newman',
 'Fab 5 Freddy',
 'Josh Greenbaum',
 'Ramón Salazar',
 'Jamie Linden',
 'Mark Dindal',
 'Jesse Dylan',
 'nan',
 'nan',
 'Lance Bangs',
 'Leslie Small,Tim Story',
 'Dean Parisot',
 'Benson Lee',
 'Karen Moncrieff',
 'Dimitri Logothetis',
 'Abby Epstein',
 'Sebastián Hofmann',
 'Oliver Parker',
 'Dylan Mohan Gray',
 'Zoya Akhtar',
 'Toa Fraser',
 'Chad Hartigan',
 'Joe Piscatella',
 'Imtiaz Ali',
 'Jamie M. Dagg',
 'Atom Egoyan',
 'J. Davis',
 'Brandon Camp',
 'Nancy Schwartzman',
 'Eva Vives',
 'David Mirkin',
 'David Sington,Heather Walsh',
 'Leslie Small,Tim Story',
 'Scott Aukerman,Akiva Schaffer',
 'Joe Robert Cole',
 'Tony Elliott',
 'Bruce Malmuth,Gary Nelson',
 'Carlos Vermut',
 'Andrzej Bartkowiak',
 'Ho-Cheung Pang',
 'Johannes Roberts',
 'Vikramaditya Motwane',
 'Mahmoud Sabbagh',
 'Mark Steven Johnson',
 'Chris Smith',
 "Mark O'Connor",
 'Dibakar Banerjee',
 'Ian Samuels',
 'Michael Carney',
 'Don Mancini',
 'S.S. Wilson',
 'Timo Tjahjanto',
 'Hae Young Jung,Young Kyun Park,Jhonen Vasquez',
 'Aleksey German Jr.',
 'Lana Wachowski,Lilly Wachowski',
 'David Pastor,Àlex Pastor',
 'Banjong Pisanthanakun,Parkpoom Wongpoom',
 'Katy Chevigny,Ross Kauffman',
 'Eric Stoltz',
 'Fritz Böhm',
 'Dan Gilroy',
 'Joe Swanberg',
 'Storm Saulter',
 'Fernando González Molina',
 'Jared Hess',
 'John Scheinfeld',
 'Emmanuel Mouret',
 'Tinto Brass',
 'Madeleine Sami,Jackie van Beek',
 'Jay Roach',
 'Seth Barrish',
 'Spike Lee',
 'Monika Mitchell',
 'Greg Whiteley',
 'Damien Leone',
 'Paul W.S. Anderson',
 'Gavin Fitzgerald',
 'Shashanka Ghosh',
 'Todd Holland',
 'Jared Moshe',
 'Farah Khan',
 'Jennifer M. Kroot',
 'Peter Chelsom',
 'Philipp Stölzl',
 'Vijay Lalwani',
 'Sally Potter',
 'Elizabeth Wood',
 'Simon Wells',
 'David Wnendt',
 'Joel Gallen,Tig Notaro',
 'Karan Johar',
 'Min-ho Woo',
 'Jennifer Baichwal,Nicholas de Pencier',
 'David Cronenberg',
 'So Yong Kim',
 'Joe Dante,Eric Goldberg',
 'Beyoncé,Ed Burke',
 'Alex Zamm',
 'Luke Sparke',
 'Lynn Shelton',
 'Michèle Ohayon',
 'J.J. Englert,Robert Krantz',
 'Sofia Coppola',
 'Tetsuo Yajima',
 'Israel Adrián Caetano',
 'Florian Henckel von Donnersmarck',
 'Cory Barlog',
 'Joshua Z Weinstein',
 'Jenna Laurenzo',
 'Sonia Kennebeck',
 'Nora Fingscheidt',
 'Chia-Liang Liu',
 'Dennie Gordon',
 'Ben Shelton',
 'Davis Guggenheim',
 'Ribhu Dasgupta',
 'Andy Fickman',
 'Erik Nelson',
 'Hrishikesh Mukherjee',
 'Nicole Holofcener',
 'John Fortenberry',
 'Hideaki Anno,Masayuki,Kazuya Tsurumaki',
 'Álex de la Iglesia',
 'Paul Andrew Williams',
 'Charlie McDowell',
 'Steven Soderbergh',
 'Hrishikesh Mukherjee',
 'Justin McConnell',
 'David Guy Levy',
 'Yilmaz Erdogan',
 'Yilmaz Erdogan,Ömer Faruk Sorak',
 'Christopher Storer',
 'Benedict Andrews',
 'Aaron Lieber',
 'Vincent Perez',
 'Kiran Rao',
 'Gerard McMurray',
 'Karim Amer,Jehane Noujaim',
 'Max Joseph',
 'nan',
 'Rhys Thomas',
 'Igor Kovalyov,Norton Virgien',
 'Jay Karas',
 'David Alvarado,Jason Sussberg',
 'David Palmer,Dax Shepard',
 'Sandeep Reddy Vanga',
 'Ian Cheney',
 'Nathan Morlando',
 'Jay Karas',
 'Christina Clusiau,Shaul Schwarz',
 'Greg MacGillivray',
 'Shree Narayan Singh',
 'Wim Wenders',
 'Trent Haaga',
 'Ana Kokkinos',
 'Toshiyuki Kubooka',
 'Bilal Lashari',
 'Daniel Lee',
 'Maria Ripoll',
 'Jesse V. Johnson',
 'Dave Green',
 'Sarah Adina Smith',
 'Shrihari Sathe',
 'Michael Lehmann',
 'Paul Dugdale',
 'Lance Bangs',
 'Kyoko Miyake',
 'Priyadarshan',
 'Anurag Kashyap',
 'Rhys Thomas',
 'nan',
 'Roland Joffé',
 "Liam O'Donnell",
 'James McTeigue',
 'John Erick Dowdle',
 'nan',
 'Julius Onah',
 'Peter Landesman',
 'Abe Forsythe',
 'Mani Ratnam',
 'Toshiyuki Kubooka',
 'Jon Amiel',
 'Abhishek Varman',
 'Bruce Robinson',
 'Anthony Wonke',
 'Marja-Lewis Ryan',
 'Michael Tolajian',
 'Rachel Lears',
 'Shaad Ali',
 'Neil Triffett',
 'Skye Borgman',
 'Billy Ray',
 'Alain Desrochers',
 'Alfonso Cuarón',
 'Dharmendra Suresh Desai',
 'Jesse V. Johnson',
 'Tinge Krishnan',
 'Sung-hoon Kim',
 'Gabe Ibáñez',
 'Alan Rickman',
 'Troy Nixey',
 'Amol Palekar',
 'Mike Diva,Akiva Schaffer',
 'Jennifer Kaytin Robinson',
 'Terry Hughes,Ian MacNaughton',
 'Mitzi Vanessa Arreola,Amir Galván Cervera',
 'Rod Blackhurst,Brian McGinn',
 'David Lee Miller',
 'Andrew Niccol',
 'Gerard Barrett',
 'Shanker Raman',
 'Jon Lucas,Scott Moore',
 'Keenen Ivory Wayans',
 'Tom Dey',
 'Claire Scanlon',
 'Doron Paz,Yoav Paz',
 'Ricky Gervais',
 'Alejandro Doria',
 'Mati Diop',
 'Liz Garbus',
 'Woody Harrelson',
 'James Mangold',
 'Bill Oliver',
 'Rohit Shetty',
 'Toshiyuki Kubooka',
 'Marti Noxon',
 'Pa. Ranjith',
 'Robert Altman',
 'Mahesh Manjrekar',
 'Christopher Smith',
 'Lauren Miller Rogen',
 'David Michôd',
 'Sydney Freeland',
 'Rod Blackhurst',
 'Roger Kumble',
 'Andy Tennant',
 'Johanna Demetrakas',
 'Kevin Rodney Sullivan',
 'Ayan Mukherjee',
 'Brian Knappenberger',
 'Shannon Hartman',
 'Maya Forbes',
 'Andrew Lau,Alan Mak,Ralph Rieckermann',
 'nan',
 'Jared Stern',
 'Francesco Lettieri',
 'Garry Marshall',
 'George Stevens',
 'Prosit Roy',
 'Baran bo Odar',
 'Ken Marino',
 'Christopher Smith',
 'Louis C.K.',
 'Mark Waters',
 'Chris Bolan',
 'John Michael McDonagh',
 'Farhan Akhtar',
 'Clark Johnson',
 'Drake Doremus',
 'Gillian Armstrong',
 'Abbas Tyrewala',
 'Kirby Dick',
 'Alex Infascelli',
 'Jill Bauer,Ronna Gradus',
 'Abhishek Kapoor',
 'Karan Malhotra',
 'Anthony Caronna,Alexander Smith',
 'Linda Saffire,Adam Schlesinger',
 'Farhan Akhtar',
 'Chris Perkel',
 'Martin Scorsese',
 'Ari Sandel',
 'Todor Chapkanov',
 'Greg MacGillivray',
 'Fernando Coimbra',
 'Mae Czarina Cruz',
 'Vincenzo Natali',
 'Leslie Small',
 'Errol Morris',
 'Elliot Hegarty',
 'Alik Sakharov',
 ...]
In [69]:
for xdir in director_list:
    current_directors=xdir.split(',')
    for xd in current_directors:
        if xd in directors_moviescount:
            directors_moviescount[xd]+= 1
        else:
            directors_moviescount[xd]=1
In [70]:
directors_moviescount
Out[70]:
{'Christopher Nolan': 4,
 'Lana Wachowski': 5,
 'Lilly Wachowski': 5,
 'Anthony Russo': 5,
 'Joe Russo': 6,
 'Robert Zemeckis': 8,
 'Sergio Leone': 2,
 'Bob Persichetti': 1,
 'Peter Ramsey': 1,
 'Rodney Rothman': 1,
 'Roman Polanski': 7,
 'Quentin Tarantino': 7,
 'Steven Spielberg': 10,
 'Martin Scorsese': 7,
 'Rajkumar Hirani': 3,
 'Guillermo del Toro': 1,
 'Lenny Abrahamson': 2,
 'Terry Gilliam': 3,
 'Terry Jones': 4,
 'Harold Ramis': 2,
 'Tom Hooper': 4,
 'Spike Jonze': 3,
 'Paul Thomas Anderson': 5,
 'David Fincher': 5,
 'Jean-Marc Vallée': 2,
 'Stephen Chbosky': 2,
 'Alex Garland': 2,
 'David O. Russell': 5,
 'Neill Blomkamp': 2,
 'Duncan Jones': 2,
 'Noah Baumbach': 6,
 'Nicolas Winding Refn': 2,
 'Sang-ho Yeon': 4,
 'Brad Bird': 3,
 'Nitesh Tiwari': 2,
 'Sergio Pablos': 1,
 'Carlos Martínez López': 1,
 'Clint Eastwood': 3,
 'Aamir Khan': 1,
 'Amole Gupte': 1,
 'John Hughes': 1,
 'Pierre Coffin': 1,
 'Chris Renaud': 3,
 'David Mackenzie': 6,
 'Frank Miller': 1,
 'Robert Rodriguez': 4,
 'Wilson Yip': 4,
 'James Marsh': 2,
 'Gabriele Muccino': 1,
 'Asif Kapadia': 2,
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 'Nathan Morlando': 1,
 'Christina Clusiau': 1,
 'Shaul Schwarz': 3,
 'Greg MacGillivray': 4,
 'Shree Narayan Singh': 1,
 'Wim Wenders': 3,
 'Trent Haaga': 1,
 'Ana Kokkinos': 1,
 'Toshiyuki Kubooka': 3,
 'Bilal Lashari': 1,
 'Daniel Lee': 1,
 'Maria Ripoll': 3,
 'Jesse V. Johnson': 4,
 'Dave Green': 1,
 'Sarah Adina Smith': 3,
 'Shrihari Sathe': 1,
 'Michael Lehmann': 2,
 'Kyoko Miyake': 1,
 'Priyadarshan': 12,
 'Anurag Kashyap': 3,
 'Roland Joffé': 4,
 "Liam O'Donnell": 1,
 'James McTeigue': 2,
 'John Erick Dowdle': 2,
 'Julius Onah': 2,
 'Peter Landesman': 2,
 'Abe Forsythe': 1,
 'Jon Amiel': 3,
 'Abhishek Varman': 2,
 'Bruce Robinson': 2,
 'Anthony Wonke': 1,
 'Marja-Lewis Ryan': 1,
 'Michael Tolajian': 1,
 'Rachel Lears': 1,
 'Shaad Ali': 4,
 'Neil Triffett': 1,
 'Skye Borgman': 2,
 'Billy Ray': 1,
 'Alain Desrochers': 2,
 'Dharmendra Suresh Desai': 2,
 'Tinge Krishnan': 1,
 'Sung-hoon Kim': 1,
 'Gabe Ibáñez': 1,
 'Alan Rickman': 1,
 'Troy Nixey': 1,
 'Amol Palekar': 1,
 'Mike Diva': 1,
 'Jennifer Kaytin Robinson': 1,
 'Terry Hughes': 2,
 'Ian MacNaughton': 2,
 'Mitzi Vanessa Arreola': 1,
 'Amir Galván Cervera': 1,
 'Rod Blackhurst': 2,
 'Brian McGinn': 2,
 'David Lee Miller': 1,
 'Andrew Niccol': 2,
 'Gerard Barrett': 1,
 'Shanker Raman': 1,
 'Jon Lucas': 1,
 'Scott Moore': 1,
 'Keenen Ivory Wayans': 1,
 'Tom Dey': 2,
 'Claire Scanlon': 2,
 'Doron Paz': 1,
 'Yoav Paz': 1,
 'Alejandro Doria': 1,
 'Mati Diop': 1,
 'Woody Harrelson': 1,
 'Bill Oliver': 2,
 'Rohit Shetty': 4,
 'Marti Noxon': 1,
 'Pa. Ranjith': 2,
 'Mahesh Manjrekar': 3,
 'Christopher Smith': 3,
 'Lauren Miller Rogen': 1,
 'Sydney Freeland': 1,
 'Johanna Demetrakas': 2,
 'Kevin Rodney Sullivan': 2,
 'Ayan Mukherjee': 1,
 'Brian Knappenberger': 2,
 'Shannon Hartman': 12,
 'Maya Forbes': 2,
 'Andrew Lau': 5,
 'Alan Mak': 1,
 'Ralph Rieckermann': 1,
 'Jared Stern': 1,
 'Francesco Lettieri': 1,
 'Garry Marshall': 6,
 'George Stevens': 6,
 'Prosit Roy': 1,
 'Baran bo Odar': 2,
 'Ken Marino': 2,
 'Chris Bolan': 1,
 'John Michael McDonagh': 2,
 'Clark Johnson': 1,
 'Gillian Armstrong': 1,
 'Abbas Tyrewala': 1,
 'Kirby Dick': 4,
 'Alex Infascelli': 1,
 'Jill Bauer': 1,
 'Ronna Gradus': 1,
 'Karan Malhotra': 1,
 'Anthony Caronna': 1,
 'Alexander Smith': 1,
 'Linda Saffire': 1,
 'Adam Schlesinger': 1,
 'Chris Perkel': 1,
 'Todor Chapkanov': 3,
 'Fernando Coimbra': 1,
 'Mae Czarina Cruz': 4,
 'Vincenzo Natali': 2,
 'Elliot Hegarty': 1,
 'Alik Sakharov': 1,
 'Michelangelo Antonioni': 3,
 'April Mullen': 2,
 'Heidi Ewing': 2,
 'Rachel Grady': 2,
 'Natalie Portman': 2,
 'Gareth Edwards': 2,
 'Fred Schepisi': 2,
 'Brian De Palma': 4,
 'Andrew Bujalski': 4,
 'Tim Hill': 1,
 'Kiké Maíllo': 1,
 'Ricki Stern': 1,
 'Anne Sundberg': 1,
 'Luis Alfaro': 1,
 'Pablo Lejarreta': 1,
 'Jay Chapman': 36,
 'Noriyuki Abe': 2,
 'Tyler Perry': 8,
 'Bobby Farrelly': 1,
 'Jonathan Sobol': 1,
 'Ernie Barbarash': 5,
 'Oliver Daly': 2,
 'Hatem Khraiche': 1,
 'Barry Sonnenfeld': 1,
 'V. Scott Balcerek': 1,
 'Yasushi Kawamura': 1,
 "Kei'ichi Sato": 1,
 'Hark Tsui': 4,
 "Federico D'Alessandro": 1,
 'Lena Khan': 1,
 'Kathleen Hepburn': 2,
 'Elle-Máijá Tailfeathers': 1,
 'Rami Hachache': 2,
 'Steven R. Monroe': 8,
 'Dash Shaw': 1,
 'Kunihiko Yuyama': 6,
 'Daniel Gray Longino': 2,
 'Fabio Guaglione': 1,
 ...}
In [71]:
DirCount=pd.DataFrame(directors_moviescount.items(), columns=['Directors','Count'])
In [72]:
DirCount
Out[72]:
Directors Count
0 Christopher Nolan 4
1 Lana Wachowski 5
2 Lilly Wachowski 5
3 Anthony Russo 5
4 Joe Russo 6
... ... ...
12449 Edward M. Abroms 1
12450 Molly Hermann 1
12451 Bernard McEveety 1
12452 Dereck Joubert 1
12453 Richard Slater-Jones 1

12454 rows × 2 columns

In [73]:
DirCount=DirCount.sort_values(ascending=False, by='Count').head(20)
In [74]:
DirCount
Out[74]:
Directors Count
56 nan 726
977 Jay Chapman 36
8166 Joseph Kane 31
1103 Cheh Chang 29
6950 William Beaudine 23
4827 Jim Wynorski 23
8665 Sam Newfield 22
8519 David DeCoteau 21
1414 Raúl Campos 21
136 Jay Karas 21
1415 Jan Suter 21
471 Marcus Raboy 18
2575 Fred Olen Ray 18
7857 William Witney 17
2103 Scott L. Montoya 17
2303 Mark Atkins 16
7965 Lesley Selander 16
1260 Manny Rodriguez 15
12317 Paul Hoen 15
6864 Robert Stevenson 14
In [75]:
DirCount.drop(56, axis=0, inplace=True)
In [76]:
DirCount
Out[76]:
Directors Count
977 Jay Chapman 36
8166 Joseph Kane 31
1103 Cheh Chang 29
6950 William Beaudine 23
4827 Jim Wynorski 23
8665 Sam Newfield 22
8519 David DeCoteau 21
1414 Raúl Campos 21
136 Jay Karas 21
1415 Jan Suter 21
471 Marcus Raboy 18
2575 Fred Olen Ray 18
7857 William Witney 17
2103 Scott L. Montoya 17
2303 Mark Atkins 16
7965 Lesley Selander 16
1260 Manny Rodriguez 15
12317 Paul Hoen 15
6864 Robert Stevenson 14
In [77]:
fig18= px.bar(x=DirCount['Directors'],
              y=DirCount['Count'],
              title='Count of movies directed by 20 first directors',
              text=DirCount['Count'],
              height=600)
fig18.update_traces(texttemplate='%{text:.2s}', textposition='outside')
fig18

Exploring Genres¶

In [78]:
genres_ =dict(df['Genres'].value_counts())
In [79]:
genres_
Out[79]:
{'Drama': 1341,
 'Documentary': 1229,
 'Comedy': 1040,
 'Comedy,Drama': 446,
 'Horror': 436,
 'Drama,Romance': 397,
 'Comedy,Drama,Romance': 333,
 'Horror,Thriller': 297,
 'Comedy,Romance': 289,
 'Drama,Thriller': 222,
 'Thriller': 211,
 'Action,Drama': 173,
 'Crime,Drama,Thriller': 169,
 'Crime,Drama': 149,
 'Action': 146,
 'Documentary,Comedy': 142,
 'Documentary,Music': 140,
 'Horror,Mystery,Thriller': 140,
 'Comedy,Horror': 126,
 'Action,Thriller': 119,
 'Western': 116,
 'Action,Crime,Drama,Thriller': 116,
 'Family': 104,
 'Horror,Sci-Fi': 101,
 'Documentary,Biography': 101,
 'Drama,Mystery,Thriller': 99,
 'Action,Crime,Thriller': 92,
 'Crime,Drama,Mystery,Thriller': 90,
 'Drama,Family': 89,
 'Biography,Drama': 87,
 'Action,Crime,Drama': 82,
 'Drama,Horror,Thriller': 81,
 'Documentary,History': 81,
 'Action,Drama,Thriller': 73,
 'Drama,War': 70,
 'Comedy,Family': 66,
 'Romance': 62,
 'Action,Comedy': 57,
 'Documentary,Sport': 56,
 'Drama,Music': 55,
 'Documentary,Short': 52,
 'Drama,Horror,Mystery,Thriller': 52,
 'Comedy,Drama,Family': 52,
 'Documentary,Biography,History': 50,
 'Animation': 49,
 'Documentary,Drama': 49,
 'Action,Adventure,Sci-Fi': 48,
 'Animation,Adventure,Comedy,Family,Fantasy': 47,
 'Mystery,Thriller': 47,
 'Action,Adventure': 47,
 'Comedy,Crime': 46,
 'Horror,Sci-Fi,Thriller': 46,
 'Action,Sci-Fi': 46,
 'Horror,Mystery': 45,
 'Sci-Fi': 45,
 'Biography,Drama,History': 44,
 'Drama,Sport': 41,
 'Crime,Drama,Romance': 40,
 'Drama,History': 40,
 'Crime,Thriller': 40,
 'Action,Adventure,Comedy,Horror,Sci-Fi,Thriller': 39,
 'Adventure,Drama': 38,
 'Action,Adventure,Fantasy': 38,
 'Drama,Music,Romance': 36,
 'Adventure': 36,
 'Drama,Horror': 36,
 'Drama,History,War': 35,
 'Crime,Drama,Mystery': 35,
 'Action,Adventure,Sci-Fi,Thriller': 35,
 'Documentary,Biography,Music': 35,
 'Drama,Mystery': 34,
 'Action,Drama,War': 34,
 'Crime,Mystery,Thriller': 34,
 'Animation,Adventure,Comedy,Family': 33,
 'Comedy,Crime,Drama': 33,
 'Comedy,Family,Fantasy': 32,
 'Action,Crime': 32,
 'Music': 31,
 'Action,Sci-Fi,Thriller': 31,
 'Drama,Sci-Fi': 31,
 'Sci-Fi,Thriller': 30,
 'Drama,Family,Romance': 29,
 'Adventure,Drama,Family': 29,
 'Drama,Romance,War': 29,
 'Documentary,Crime': 29,
 'Short,Drama': 29,
 'Comedy,Music': 28,
 'Animation,Family': 27,
 'Documentary,History,War': 27,
 'Documentary,War': 27,
 'Action,Comedy,Drama': 26,
 'Crime': 26,
 'Biography,Drama,Sport': 25,
 'Comedy,Horror,Sci-Fi': 25,
 'Drama,Musical,Romance': 25,
 'Comedy,Musical,Romance': 25,
 'Biography,Drama,Romance': 25,
 'Action,Drama,Romance': 24,
 'Documentary,News': 24,
 'Drama,Romance,Thriller': 24,
 'Action,Adventure,Drama': 23,
 'Drama,Sci-Fi,Thriller': 23,
 'Comedy,Drama,Fantasy': 23,
 'Comedy,Fantasy,Romance': 23,
 'Action,Horror,Thriller': 22,
 'Comedy,Fantasy': 22,
 'Comedy,Family,Romance': 22,
 'Comedy,Sci-Fi': 22,
 'Action,Comedy,Crime': 21,
 'Action,Crime,Drama,Mystery,Thriller': 21,
 'Adventure,Family': 21,
 'Action,Adventure,Comedy': 20,
 'Crime,Drama,Film-Noir,Thriller': 20,
 'Drama,Western': 20,
 'Animation,Short,Family': 20,
 'Animation,Short': 19,
 'Documentary,Family': 19,
 'Action,Horror,Sci-Fi,Thriller': 19,
 'Comedy,Sport': 19,
 'Action,Adventure,Drama,Sci-Fi,Thriller': 19,
 'Comedy,Drama,Music,Romance': 18,
 'Drama,Horror,Mystery': 18,
 'Action,Adventure,Thriller': 18,
 'Action,Adventure,Horror,Sci-Fi,Thriller': 18,
 'Mystery': 18,
 'Comedy,Crime,Thriller': 18,
 'Adventure,Comedy': 18,
 'Biography,Drama,Music': 18,
 'Animation,Adventure,Family': 18,
 'Comedy,Drama,Music': 17,
 'Action,Drama,History,War': 17,
 'Animation,Adventure,Family,Fantasy': 17,
 'Short': 17,
 'Documentary,Biography,Sport': 17,
 'Drama,Family,Sport': 17,
 'Crime,Drama,Horror,Mystery,Thriller': 16,
 'Comedy,Musical': 16,
 'Adventure,Fantasy': 16,
 'Drama,Mystery,Romance': 16,
 'Comedy,Thriller': 16,
 'Short,Comedy': 16,
 'Adventure,Family,Fantasy': 16,
 'Biography,Crime,Drama': 16,
 'Crime,Horror,Thriller': 16,
 'Mystery,Sci-Fi,Thriller': 16,
 'Action,Adventure,Drama,Thriller': 16,
 'Adventure,Comedy,Drama': 15,
 'Fantasy,Horror': 15,
 'Drama,Fantasy,Romance': 15,
 'Action,Adventure,Fantasy,Sci-Fi': 15,
 'Biography,Drama,History,War': 15,
 'Comedy,Drama,Family,Romance': 15,
 'Action,Comedy,Crime,Thriller': 15,
 'Adventure,Sci-Fi': 15,
 'Action,Comedy,Horror': 15,
 'Action,Comedy,Thriller': 14,
 'Comedy,Drama,Thriller': 14,
 'Comedy,Drama,Musical,Romance': 14,
 'Comedy,Drama,Fantasy,Romance': 14,
 'Action,Drama,Western': 14,
 'Crime,Drama,Romance,Thriller': 14,
 'Biography,Comedy,Drama': 14,
 'Drama,Mystery,Sci-Fi,Thriller': 14,
 'Action,Horror': 14,
 'Comedy,Western': 14,
 'Reality-TV': 14,
 'Crime,Drama,Mystery,Romance,Thriller': 13,
 'Animation,Family,Fantasy': 13,
 'Comedy,Horror,Thriller': 13,
 'Drama,Fantasy': 13,
 'Comedy,Crime,Drama,Romance': 13,
 'Action,Comedy,Romance': 13,
 'Comedy,Music,Romance': 13,
 'Action,Adventure,Western': 13,
 'Drama,Musical': 13,
 'Drama,Fantasy,Horror,Mystery,Thriller': 13,
 'Drama,Horror,Sci-Fi,Thriller': 12,
 'Musical': 12,
 'Adventure,Comedy,Family,Fantasy': 12,
 'Romance,Western': 12,
 'Crime,Drama,Horror,Thriller': 12,
 'Action,Drama,Sport': 12,
 'Biography,Drama,History,Romance': 12,
 'Sport': 12,
 'Comedy,Drama,Family,Fantasy': 12,
 'Action,Adventure,Romance,Western': 11,
 'Comedy,Drama,War': 11,
 'Animation,Comedy,Family': 11,
 'Documentary,Adventure': 11,
 'Animation,Comedy,Family,Fantasy': 11,
 'Documentary,History,News': 11,
 'Biography': 11,
 'Action,Crime,Drama,Romance': 11,
 'Comedy,Family,Sci-Fi': 11,
 'Comedy,Mystery': 11,
 'Action,Drama,Sci-Fi,Thriller': 11,
 'Horror,Mystery,Sci-Fi,Thriller': 11,
 'Animation,Adventure,Comedy,Family,Fantasy,Musical': 11,
 'Talk-Show': 11,
 'Crime,Mystery': 10,
 'Fantasy,Horror,Thriller': 10,
 'Adventure,Drama,Romance': 10,
 'Crime,Drama,Film-Noir': 10,
 'Documentary,Biography,Drama': 10,
 'Drama,Mystery,Sci-Fi': 10,
 'Action,Horror,Sci-Fi': 10,
 'Comedy,Crime,Drama,Thriller': 10,
 'Adventure,Comedy,Family': 10,
 'History': 10,
 'Drama,Horror,Sci-Fi': 10,
 'Drama,History,Romance': 10,
 'Biography,Crime,Drama,Thriller': 10,
 'Action,Drama,History': 10,
 'Action,Adventure,Drama,Romance,Western': 10,
 'Drama,Romance,Western': 9,
 'Comedy,Drama,Sport': 9,
 'Action,Western': 9,
 'Drama,Thriller,War': 9,
 'Comedy,Drama,Musical': 9,
 'Action,Romance,Western': 9,
 'Action,Comedy,Crime,Drama': 9,
 'Comedy,Romance,Sport': 9,
 'Documentary,Biography,History,Music': 9,
 'Adventure,Drama,Fantasy': 9,
 'Comedy,Short': 9,
 'Documentary,Comedy,Drama': 9,
 'Family,Fantasy': 9,
 'Action,Crime,Mystery,Thriller': 9,
 'Adventure,Horror,Thriller': 9,
 'Fantasy,Horror,Mystery,Thriller': 9,
 'Fantasy': 9,
 'Comedy,Fantasy,Horror': 9,
 'Action,Comedy,Crime,Drama,Thriller': 9,
 'Action,Adventure,Comedy,Crime': 9,
 'Action,Drama,Sci-Fi': 8,
 'Drama,Film-Noir': 8,
 'Action,Adventure,Comedy,Sci-Fi': 8,
 'Action,Adventure,Comedy,Fantasy,Horror,Sci-Fi,Thriller': 8,
 'Action,Comedy,Drama,Romance': 8,
 'Animation,Adventure,Comedy,Family,Musical': 8,
 'Action,Fantasy,Horror': 8,
 'Action,Music,Western': 8,
 'Action,Adventure,Music,Western': 8,
 'Action,Drama,Romance,Thriller': 8,
 'Adventure,Romance': 8,
 'Biography,Crime,Drama,History': 8,
 'Comedy,Family,Sport': 8,
 'Comedy,Crime,Romance': 8,
 'Drama,Horror,Mystery,Sci-Fi,Thriller': 8,
 'Crime,Horror,Mystery,Thriller': 8,
 'Comedy,Drama,Horror': 8,
 'Drama,Mystery,Romance,Thriller': 8,
 'Action,Drama,Mystery,Thriller': 8,
 'Drama,Comedy': 8,
 'Adventure,Comedy,Drama,Family': 8,
 'Action,Adventure,Crime,Drama,Thriller': 8,
 'Adventure,Horror': 8,
 'Adventure,Comedy,Fantasy': 8,
 'Biography,Drama,War': 8,
 'Documentary,Horror': 8,
 'Drama,Romance,Sci-Fi': 8,
 'Drama,Fantasy,Horror,Thriller': 8,
 'Comedy,Family,Fantasy,Musical': 7,
 'Family,Romance': 7,
 'Horror,Mystery,Sci-Fi': 7,
 'Action,Adventure,Drama,Western': 7,
 'Action,Drama,Romance,War': 7,
 'Comedy,Family,Music': 7,
 'Drama,History,Thriller': 7,
 'Comedy,Romance,Western': 7,
 'Action,Adventure,Crime,Thriller': 7,
 'Documentary,Crime,History': 7,
 'Crime,Film-Noir,Thriller': 7,
 'Action,Adventure,Drama,Fantasy': 7,
 'Animation,Adventure,Comedy,Family,Fantasy,Sci-Fi': 7,
 'Animation,Action,Adventure,Comedy,Family,Fantasy': 7,
 'Action,Adventure,Horror,Thriller': 7,
 'Documentary,History,Sport': 7,
 'Drama,Fantasy,Horror': 7,
 'Drama,Romance,Sport': 7,
 'Drama,Family,Fantasy': 7,
 'Adventure,Comedy,Family,Sci-Fi': 7,
 'Action,Comedy,Music,Western': 7,
 'Comedy,War': 7,
 'Documentary,Biography,History,News': 6,
 'Drama,History,Romance,War': 6,
 'Action,Drama,Mystery': 6,
 'Comedy,Horror,Romance': 6,
 'Adventure,Comedy,Sci-Fi': 6,
 'Comedy,Family,Fantasy,Romance': 6,
 'Adventure,Drama,Thriller': 6,
 'Action,Crime,Horror,Thriller': 6,
 'Crime,Drama,Mystery,Romance': 6,
 'Crime,Drama,Film-Noir,Mystery': 6,
 'Action,Adventure,Family': 6,
 'Comedy,Drama,Horror,Thriller': 6,
 'Action,Crime,Drama,Romance,Thriller': 6,
 'Adventure,Western': 6,
 'Action,Fantasy': 6,
 'Drama,Family,Musical,Romance': 6,
 'Biography,Drama,Family': 6,
 'Action,Adventure,Family,Fantasy': 6,
 'Comedy,Drama,Family,Sport': 6,
 'Documentary,Biography,Drama,News': 6,
 'Animation,Action,Adventure,Family,Fantasy': 6,
 'Documentary,Biography,Drama,History': 6,
 'Drama,Fantasy,Mystery': 6,
 'Action,Comedy,Horror,Sci-Fi,Thriller': 6,
 'Action,Adventure,Crime,Drama': 6,
 'Documentary,Biography,Comedy': 6,
 'Comedy,Drama,Mystery': 6,
 'Short,Family': 6,
 'Comedy,Drama,Western': 6,
 'Comedy,Drama,Romance,Thriller': 6,
 'Animation,Action,Adventure,Fantasy': 6,
 'Adventure,Crime,Drama,Mystery,Thriller': 6,
 'Action,Adventure,Drama,Sci-Fi': 5,
 'Drama,Family,Music': 5,
 'Family,Musical': 5,
 'Action,Adventure,Fantasy,Thriller': 5,
 'Adventure,Comedy,Drama,Family,Fantasy': 5,
 'Animation,Action,Adventure,Comedy,Family,Sci-Fi': 5,
 'Action,Comedy,Family': 5,
 'Comedy,Crime,Mystery': 5,
 'Comedy,Crime,Horror,Thriller': 5,
 'Adventure,Drama,History,Romance': 5,
 'Action,Fantasy,Horror,Thriller': 5,
 'Action,Drama,Romance,Western': 5,
 'Comedy,Mystery,Thriller': 5,
 'Animation,Action,Sci-Fi': 5,
 'Animation,Adventure,Comedy,Family,Sci-Fi': 5,
 'Romance,Thriller': 5,
 'Documentary,Biography,History,War': 5,
 'Short,Music': 5,
 'Adventure,Comedy,Horror': 5,
 'Thriller,Drama': 5,
 'Action,Adventure,Comedy,Drama': 5,
 'Action,Comedy,Fantasy,Horror': 5,
 'Comedy,Documentary': 5,
 'Animation,Family,Fantasy,Musical': 5,
 'Animation,Fantasy': 5,
 'Adventure,Horror,Sci-Fi': 5,
 'Action,Drama,History,Romance,War': 5,
 'Animation,Short,Comedy,Family,Fantasy': 5,
 'Musical,Romance': 5,
 'Animation,Adventure,Comedy,Family,Fantasy,Musical,Romance': 5,
 'Drama,Fantasy,Sci-Fi': 5,
 'Documentary,Crime,News': 5,
 'Comedy,Horror,Sci-Fi,Thriller': 5,
 'News': 5,
 'Comedy,Horror,Mystery,Thriller': 5,
 'Mystery,Romance,Thriller': 5,
 'Documentary,Drama,History': 5,
 'Comedy,Music,Western': 5,
 'Animation,Action,Adventure,Family,Sci-Fi': 5,
 'Family,Fantasy,Musical': 5,
 'Documentary,Romance': 5,
 'Comedy,Crime,Mystery,Thriller': 5,
 'Adventure,Comedy,Romance': 5,
 'Action,Adventure,Drama,Romance,War': 5,
 'Documentary,History,Music': 5,
 'Adventure,Drama,Horror,Thriller': 5,
 'Documentary,Short,Family': 5,
 'Animation,Adventure': 5,
 'Action,Drama,Thriller,War': 5,
 'Documentary,Adventure,Sport': 5,
 'Action,Drama,History,Thriller': 5,
 'Action,Mystery,Thriller': 5,
 'Adventure,Family,Sci-Fi': 5,
 'Action,Adventure,War': 5,
 'Comedy,Drama,Romance,Sport': 5,
 'Documentary,Mystery': 5,
 'Documentary,Biography,Drama,Family': 5,
 'Crime,Drama,History': 5,
 'Family,Sci-Fi': 5,
 'Drama,Fantasy,Mystery,Thriller': 5,
 'Music,Western': 5,
 'War': 5,
 'Fantasy,Horror,Mystery': 5,
 'Action,Adventure,Drama,Horror,Sci-Fi,Thriller': 5,
 'Animation,Action,Adventure': 5,
 'Animation,Short,Adventure,Family,Fantasy': 5,
 'Documentary,Western': 5,
 'Mystery,Romance': 5,
 'Comedy,Drama,Fantasy,Horror': 4,
 'Fantasy,Sci-Fi': 4,
 'Documentary,Animation': 4,
 'Action,Comedy,Horror,Thriller': 4,
 'Short,Action': 4,
 'Drama,Fantasy,Mystery,Romance,Thriller': 4,
 'Adventure,Drama,Western': 4,
 'Action,Drama,Horror,Thriller': 4,
 'Documentary,History,News,War': 4,
 'Animation,Family,Musical': 4,
 'Action,Adventure,Comedy,Fantasy': 4,
 'Short,Thriller': 4,
 'Family,Sport': 4,
 'Documentary,Short,War': 4,
 'Animation,Adventure,Comedy': 4,
 'Crime,Drama,Western': 4,
 'Documentary,Short,Biography': 4,
 'Adventure,Comedy,Drama,Romance': 4,
 'Biography,Crime,Drama,Romance': 4,
 'Adventure,Fantasy,Sci-Fi': 4,
 'Documentary,Adventure,Drama,News': 4,
 'Biography,Comedy,Crime,Drama': 4,
 'Short,Comedy,Drama,Romance': 4,
 'Action,Crime,Drama,History,Thriller': 4,
 'Action,Thriller,War': 4,
 'Animation,Adventure,Family,Fantasy,Musical': 4,
 'Action,Romance,Thriller': 4,
 'Adventure,Drama,History,War': 4,
 'Adventure,Crime,Thriller': 4,
 'Biography,Drama,Music,Romance': 4,
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 'Comedy,Crime,Romance,Thriller': 1,
 'Action,Family,Sci-Fi': 1,
 'Comedy,Drama,Fantasy,Horror,Thriller': 1,
 'Horror,Sci-Fi,Mystery': 1,
 'Musical,Drama,Romance,Family': 1,
 'Action,Adventure,Comedy,Romance,Western': 1,
 'Adventure,Comedy,Music,Musical,Romance': 1,
 'Short,Mystery,Sci-Fi': 1,
 'Animation,Comedy,Family,Music': 1,
 'Crime,Drama,Horror,Mystery': 1,
 'Action,Drama,Fantasy,Romance': 1,
 'Action,Drama,Family,Western': 1,
 'Animation,Action,Music,Western': 1,
 'Action,Adventure,Fantasy,Horror,Sci-Fi': 1,
 'Action,Adventure,Comedy,Horror,Romance': 1,
 'Action,Sport,Thriller': 1,
 'Adventure,Drama,Music': 1,
 'Biography,Drama,Romance,War,Western': 1,
 'Action,Fantasy,Sci-Fi,Thriller': 1,
 'Short,Adventure,Drama,Thriller': 1,
 'Drama,Short,Fantasy': 1,
 'Drama,Mystery,Romance,Sport': 1,
 'Biography,Comedy,Drama,Music,Romance': 1,
 'Short,Animation,Family': 1,
 'Animation,Adventure,Comedy,Western': 1,
 'Short,Drama,Sci-Fi,Thriller': 1,
 'Drama,Fantasy,History': 1,
 'Comedy,Drama,Family,Mystery,Romance': 1,
 'Action,Horror,Mystery,Sci-Fi': 1,
 'Documentary,Biography,Crime,History,Sport': 1,
 'Animation,Action,Adventure,Drama,Family,Horror,Mystery,Sci-Fi': 1,
 'Comedy,Drama,Fantasy,Musical,Romance': 1,
 'Action,Adventure,Comedy,Crime,Romance,Western': 1,
 'Action,Adventure,Drama,Mystery,Thriller,War': 1,
 'Horror,Sci-Fi,Western': 1,
 'Short,Comedy,Music,Sport': 1,
 'Documentary,Drama,Mystery,Thriller': 1,
 'Drama,Fantasy,Western': 1,
 'Action,Adventure,Crime,Drama,Western': 1,
 'Action,Drama,Music,Western': 1,
 'Documentary,Drama,Fantasy,Horror': 1,
 'Crime,Mystery,Romance': 1,
 'History,Horror,Thriller': 1,
 'Western,Horror': 1,
 'Action,Music': 1,
 'Action,Fantasy,Western': 1,
 'Crime,Sci-Fi,Thriller': 1,
 'Adventure,Drama,War,Western': 1,
 'Drama,Adventure': 1,
 'Adventure,Drama,Fantasy,Thriller,Western': 1,
 'Fantasy,Horror,Musical,Sci-Fi': 1,
 'Action,Comedy,Fantasy,Horror,Mystery,Thriller': 1,
 'Crime,Romance,Western': 1,
 'Action,Adventure,Comedy,Drama,Family,Fantasy,Horror,Sci-Fi,Thriller': 1,
 'Adventure,Comedy,Drama,Thriller': 1,
 'Drama,Action,Thriller': 1,
 'Animation,Adventure,Comedy,Drama,Family,Mystery,Sci-Fi,Thriller': 1,
 'Action,Crime,Drama,Western': 1,
 'Documentary,Drama,Family,History': 1,
 'Comedy,Family,Mystery': 1,
 'Animation,History': 1,
 'Action,Adventure,Comedy,Fantasy,Western': 1,
 'Animation,Comedy,Musical': 1,
 'Sci-Fi,Drama': 1,
 'Animation,Adventure,Comedy,Family,Sci-Fi,Sport': 1,
 'Documentary,Adventure,Western': 1,
 'Documentary,Family,History,Music': 1,
 'Action,Comedy,Musical': 1,
 'Action,Adventure,Drama,Family,Fantasy': 1,
 'Music,Romance,Western': 1,
 'Film-Noir,Mystery': 1,
 'Family,Horror,Thriller': 1,
 'Action,Adventure,Crime,Music,Western': 1,
 'Crime,Drama,Mystery,Sci-Fi': 1,
 'Documentary,Short,Drama,Family,War': 1,
 'Adventure,History,Romance,War,Western': 1,
 'Action,Crime,Romance': 1,
 'Documentary,Animation,Short,Biography,Drama': 1,
 'Action,Adventure,Crime,Romance,Thriller': 1,
 'Short,Action,History,Western': 1,
 'Comedy,Drama,Music,Mystery': 1,
 'Action,Drama,Romance,Sci-Fi': 1,
 'Action,Adventure,Mystery,Western': 1,
 'Drama,Family,Mystery,Sport': 1,
 'Action,Biography,Drama,Romance,War': 1,
 'Drama,Music,Sci-Fi,Thriller': 1,
 'Action,Adventure,Crime,Fantasy,Sci-Fi': 1,
 'Action,Adventure,Crime,Mystery,Romance,Thriller': 1,
 'Action,Adventure,Drama,Fantasy,Mystery,Sci-Fi': 1,
 'Adventure,Drama,Fantasy,Thriller': 1,
 'Short,Comedy,Drama,History,Thriller': 1,
 'Family,Mystery,Sport': 1,
 'Action,Drama,Sci-Fi,Sport,Thriller': 1,
 'Adventure,Family,Comedy': 1,
 'Musical,Family,Fantasy': 1,
 ...}
In [80]:
genrescountt= dict()

for x,count in genres_.items():
    xp=x.split(',')
    for i in xp:
        if i in genrescountt:
            genrescountt[i]+=1
        else:
            genrescountt[i]=1
In [81]:
genrescountt
Out[81]:
{'Drama': 868,
 'Documentary': 249,
 'Comedy': 654,
 'Horror': 296,
 'Romance': 420,
 'Thriller': 467,
 'Action': 553,
 'Crime': 347,
 'Music': 171,
 'Mystery': 318,
 'Western': 168,
 'Family': 426,
 'Sci-Fi': 312,
 'Biography': 190,
 'History': 198,
 'War': 170,
 'Sport': 126,
 'Short': 141,
 'Animation': 265,
 'Adventure': 560,
 'Fantasy': 371,
 'Musical': 171,
 'News': 36,
 'Film-Noir': 25,
 'Reality-TV': 8,
 'Talk-Show': 8,
 'Game-Show': 6}
In [82]:
genres_df= pd.DataFrame(genrescountt.items(), columns=['Genres','Count'])
In [83]:
genres_df
Out[83]:
Genres Count
0 Drama 868
1 Documentary 249
2 Comedy 654
3 Horror 296
4 Romance 420
5 Thriller 467
6 Action 553
7 Crime 347
8 Music 171
9 Mystery 318
10 Western 168
11 Family 426
12 Sci-Fi 312
13 Biography 190
14 History 198
15 War 170
16 Sport 126
17 Short 141
18 Animation 265
19 Adventure 560
20 Fantasy 371
21 Musical 171
22 News 36
23 Film-Noir 25
24 Reality-TV 8
25 Talk-Show 8
26 Game-Show 6
In [84]:
sort_genresdf= genres_df.sort_values(ascending=False, by='Count')
sort_genresdf
Out[84]:
Genres Count
0 Drama 868
2 Comedy 654
19 Adventure 560
6 Action 553
5 Thriller 467
11 Family 426
4 Romance 420
20 Fantasy 371
7 Crime 347
9 Mystery 318
12 Sci-Fi 312
3 Horror 296
18 Animation 265
1 Documentary 249
14 History 198
13 Biography 190
8 Music 171
21 Musical 171
15 War 170
10 Western 168
17 Short 141
16 Sport 126
22 News 36
23 Film-Noir 25
24 Reality-TV 8
25 Talk-Show 8
26 Game-Show 6
In [85]:
fig19= px.bar(x= sort_genresdf['Genres'],
              y= sort_genresdf['Count'],
              title='Count of genres',
              text=sort_genresdf['Count'],
              height=600)
fig19.update_traces(texttemplate='%{text:.2s}', textposition='outside', marker_color='red')
fig19

What are the top movies on each platform ?¶

Netflix¶

In [86]:
netflixtop_movies= netflix[netflix['IMDb']>8.5].sort_values(ascending=False, by='IMDb')[['Title', 'IMDb']]
In [87]:
netflixtop_movies
Out[87]:
Title IMDb
1292 My Next Guest with David Letterman and Shah Ru... 9.3
947 Natsamrat 9.1
0 Inception 8.8
4 The Good, the Bad and the Ugly 8.8
1 The Matrix 8.7
1214 Bill Hicks: Relentless 8.7
1311 Eh Janam Tumhare Lekhe 8.7
1458 Untamed Romania 8.7
1979 One Heart: The A.R. Rahman Concert Film 8.7
845 Gol Maal 8.6
1312 Bill Hicks: Revelations 8.6
1356 Merku Thodarchi Malai 8.6
1468 K. D. 8.6
1538 Luciano Mellera: Infantiloide 8.6
2195 Hikaru Utada Laughter in the Dark Tour 2018 8.6
2704 True: Happy Hearts Day 8.6
In [88]:
fig20= px.bar(x= netflixtop_movies['Title'],
              y= netflixtop_movies['IMDb'],
              title='Top movies on Netflix by IMDb Rating',
              text=netflixtop_movies['IMDb'],
              height=800)
fig20.update_traces(texttemplate='%{text:.2s}', textposition='outside', marker_color='purple')
fig20

Hulu¶

In [89]:
hulutop_movies=hulu[hulu['IMDb']>8.5].sort_values(ascending=False, by='IMDb')[['Title','IMDb']]
hulutop_movies
Out[89]:
Title IMDb
3560 The Dark Knight 9.0
3561 GoodFellas 8.7
3562 Parasite 8.6
3564 The Green Mile 8.6
In [90]:
fig21= px.bar(x= hulutop_movies['Title'],
              y= hulutop_movies['IMDb'],
              title='Top movies on Hulu by IMDb Rating',
              text=hulutop_movies['IMDb'],
              height=600)
fig21.update_traces(texttemplate='%{text:.2s}', textposition='outside', marker_color='brown')
fig21

Amazon Prime Video¶

In [91]:
amazonprimetop_movies= prime_video[prime_video['IMDb']>8.5].sort_values(ascending=False, by='IMDb')[['Title','IMDb']]
amazonprimetop_movies
Out[91]:
Title IMDb
5110 Love on a Leash 9.3
7426 Bounty 9.3
6837 Steven Banks: Home Entertainment Center 9.3
6566 Square One 9.3
7220 Down, But Not Out! 9.3
... ... ...
5730 George Carlin: You Are All Diseased 8.6
5607 Aruvi 8.6
9004 The Legend of Pancho Barnes and the Happy Bott... 8.6
9098 Yellow Colt 8.6
7619 The New Public 8.6

80 rows × 2 columns

In [92]:
fig22= px.bar(x= amazonprimetop_movies['Title'],
              y= amazonprimetop_movies['IMDb'],
              title='Top movies on Amazon Prime Video by IMDb Rating',
              text=amazonprimetop_movies['IMDb'],
              height=800)
fig22.update_traces(texttemplate='%{text:.2s}', textposition='outside', marker_color='green')
fig22

Disney+¶

In [93]:
disneyplustop_movies = disney_plus[disney_plus['IMDb']>8.5].sort_values(ascending=False, by='IMDb')[['Title','IMDb']]
disneyplustop_movies
Out[93]:
Title IMDb
16213 Star Wars: The Empire Strikes Back 8.7
16212 Star Wars: A New Hope 8.6
In [94]:
fig23= px.bar(x= disneyplustop_movies['Title'],
              y= disneyplustop_movies['IMDb'],
              title='Top movies on Disney+ by IMDb Rating',
              text=disneyplustop_movies['IMDb'],
              height=800)
fig23.update_traces(texttemplate='%{text:.2s}', textposition='outside', marker_color='lightsalmon')
fig23
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